Long-Term Memory Manager

Long-term memory management system for maintaining MEMORY.md, consolidating daily memories, and extracting key insights. Use when: (1) Consolidating daily memories into MEMORY.md, (2) Archiving old daily memories, (3) Extracting key facts from conversations, (4) Searching through memory history, (5) Setting up memory maintenance automation. Triggers on '长期记忆', 'memory consolidate', '记忆管理', '归档', 'MEMORY.md', '记忆压缩'.

Long-Term Memory Management

Systematic management of MEMORY.md and daily memory files for persistent knowledge retention.

Memory Architecture

text

~/.openclaw/workspace/
├── MEMORY.md              # Long-term curated memory (main)
├── memory/                # Daily memory files
│   ├── 2025-01-20.md
│   ├── 2025-01-21.md
│   └── ...
└── .memory-archive/       # Archived memories
    └── 2025-01/
        ├── consolidated.md
        └── raw/

Quick Commands

bash

# Consolidate recent daily memories
python3 {baseDir}/scripts/memory_manager.py --consolidate --days 7

# Archive old memories
python3 {baseDir}/scripts/memory_manager.py --archive --older-than 30

# Extract key facts from MEMORY.md
python3 {baseDir}/scripts/memory_manager.py --extract-facts

# Search memory history
python3 {baseDir}/scripts/memory_manager.py --search "关键词"

# Generate memory summary
python3 {baseDir}/scripts/memory_manager.py --summary --output memory-summary.md

# Health check
python3 {baseDir}/scripts/memory_manager.py --health

MEMORY.md Management

Structure

MEMORY.md should contain distilled, long-term knowledge:

markdown

# MEMORY.md - Long-Term Memory

## User Profile
- Name: ...
- Preferences: ...
- Work patterns: ...

## Key Decisions
- [Date] Decision: Reasoning...

## Important Facts
- Account: location...
- Credentials: stored in...
- Recurring tasks: ...

## Lessons Learned
- Pattern: Insight...

## Active Projects
- Project A: Status, next steps...

## Recurring Context
- Weekly meetings: ...
- Regular reports: ...

Consolidation Workflow

1. Review daily memories (last 7 days)
2. Extract significant items:
   • User preferences mentioned
   • Important decisions made
   • New facts discovered
   • Lessons learned
   • Active project updates
3. Update MEMORY.md with distilled content
4. Archive processed daily files

bash

# Full consolidation workflow
python3 {baseDir}/scripts/memory_manager.py --consolidate --auto-archive

What to Keep in MEMORY.md

Keep	Don't Keep
User preferences	Temporary states
Key decisions	Daily trivia
Important facts	Transient data
Lessons learned	Detailed logs
Active projects	Heartbeat checks
Recurring patterns	One-time events
Credentials locations	OAuth URLs

Daily Memory Files

Purpose

memory/YYYY-MM-DD.md files capture:

• What happened today
• Important conversations
• Decisions made
• Tasks completed
• Context for future reference

Best Practices

DO:

markdown

# 2025-01-20

## Key Events
- User asked about X, decided Y
- Set up new integration Z
- Discovered preference for concise responses

## Decisions
- Use tool X instead of Y for Z task (user preference)

## Pending
- Follow up on ...

DON'T:

markdown

# 2025-01-20

Got message. Replied HEARTBEAT_OK.
User said hi. Said hi back.
Time is 3pm.

Automatic Extraction

The system can extract valuable content:

bash

# Extract what matters from daily files
python3 {baseDir}/scripts/memory_manager.py --extract --from "2025-01-20.md"

# Output: List of extractable facts

Archival System

Archive Threshold

Default: Archive daily memories older than 30 days

bash

# Archive old memories
python3 {baseDir}/scripts/memory_manager.py --archive --older-than 30

# Archive with consolidation
python3 {baseDir}/scripts/memory_manager.py --archive --older-than 30 --consolidate-first

Archive Structure

text

.memory-archive/
├── 2025-01/
│   ├── consolidated.md    # Summary of the month
│   └── raw/               # Original daily files
│       ├── 2025-01-01.md
│       └── ...
└── 2025-02/
    └── ...

Retrieval from Archive

bash

# Search archived memories
python3 {baseDir}/scripts/memory_manager.py --search "keyword" --include-archive

# Retrieve specific archived content
python3 {baseDir}/scripts/memory_manager.py --retrieve "2025-01-15"

Memory Compression

What Gets Compressed

Daily memories contain repetition and noise. Compression extracts:

1. Unique events - Things that happened once
2. Recurring patterns - Things that repeat
3. Key decisions - Choices made
4. Important facts - Persistent information

Compression Rules

bash

# Compress with custom rules
python3 {baseDir}/scripts/memory_manager.py --compress \
  --rules keep-decisions,keep-preferences,keep-facts \
  --remove heartbeets,trivial,transient

Example Compression

Before (daily files, 5000 words):

markdown

# 2025-01-20
User asked about API. Looked up docs. Found answer.
User preferred concise response. Noted preference.
...

# 2025-01-21
User asked about API again. Provided concise answer.
User appreciated brevity.
...

After (MEMORY.md, 100 words):

markdown

## User Preferences
- Prefers concise responses over detailed explanations

## Knowledge
- API documentation location: ...

## Lessons
- Concise answers are preferred for API questions

Memory Search

Search Commands

bash

# Search all memories
python3 {baseDir}/scripts/memory_manager.py --search "关键词"

# Search specific range
python3 {baseDir}/scripts/memory_manager.py --search "..." --from 2025-01-01 --to 2025-01-31

# Search with context
python3 {baseDir}/scripts/memory_manager.py --search "..." --context 3

# Search archives too
python3 {baseDir}/scripts/memory_manager.py --search "..." --include-archive

Search Output

json

{
  "query": "关键词",
  "results": [
    {
      "date": "2025-01-20",
      "file": "memory/2025-01-20.md",
      "line": 15,
      "context": "...",
      "relevance": "high"
    }
  ],
  "total": 3
}

Integration with Vector Memory

This skill works alongside vector memory (LanceDB):

System	Purpose	Retention
MEMORY.md	Curated long-term memory	Permanent
memory/YYYY-MM-DD.md	Daily logs	30 days → archive
Vector memory (LanceDB)	Semantic search	Variable

Coordination

bash

# Consolidate both systems
python3 {baseDir}/scripts/memory_manager.py --consolidate --sync-vector

# The script will:
# 1. Update MEMORY.md
# 2. Archive old daily files
# 3. Sync key facts to vector memory

Automation

Periodic Consolidation

Add to heartbeat or cron:

yaml

# HEARTBEAT.md
- Run memory consolidation weekly
- Archive memories older than 30 days
- Sync to vector memory

Or via cron:

bash

# Weekly consolidation (Sunday 4am)
cron action=add job='{
  "name": "memory-consolidation",
  "schedule": "0 4 * * 0",
  "text": "Consolidate weekly memories: 1) Review memory/ files 2) Update MEMORY.md 3) Archive old files 4) Sync to vector memory"
}'

Automated Extraction

During heartbeats, automatically extract:

bash

python3 {baseDir}/scripts/memory_manager.py --auto-extract --days 1

Memory Health

Health Metrics

Metric	Healthy	Warning	Critical
Daily files count	<30	30-60	>60
MEMORY.md size	<50KB	50-100KB	>100KB
Archive coverage	>90%	50-90%	<50%
Last consolidation	<7 days	7-14 days	>14 days

Health Check

bash

python3 {baseDir}/scripts/memory_manager.py --health

# Output

json

{
  "status": "healthy",
  "metrics": {
    "daily_files": 15,
    "memory_md_size": "12KB",
    "last_consolidation": "2025-01-18",
    "archive_coverage": "95%"
  },
  "recommendations": []
}

Cleanup

bash

# Clean up stale content
python3 {baseDir}/scripts/memory_manager.py --cleanup

# Remove duplicates
python3 {baseDir}/scripts/memory_manager.py --dedupe

# Reorganize structure
python3 {baseDir}/scripts/memory_manager.py --reorganize

Best Practices

Writing to MEMORY.md

1. Be concise - Distill, don't copy
2. Use structure - Consistent sections
3. Date important items - When did this become true?
4. Review periodically - Remove outdated info
5. One concept per line - Easy to search

Managing Daily Files

1. Write daily - Capture what matters
2. Skip trivial - No heartbeat logs
3. Link related - Reference other files
4. Mark important - Use clear headings
5. Archive promptly - Don't accumulate

Integration with Other Skills

bash

# Use with self-evolution
python3 {baseDir}/scripts/memory_manager.py --consolidate
python3 ../self-evolution/scripts/evolution.py --analyze --with-memory

# Use with self-improvement
# Log a learning, then consolidate
python3 {baseDir}/scripts/memory_manager.py --extract-from .learnings/LEARNINGS.md

Workflow Examples

Weekly Maintenance

bash

# 1. Check health
python3 {baseDir}/scripts/memory_manager.py --health

# 2. Consolidate recent memories
python3 {baseDir}/scripts/memory_manager.py --consolidate --days 7

# 3. Archive old files
python3 {baseDir}/scripts/memory_manager.py --archive --older-than 30

# 4. Sync to vector memory
python3 {baseDir}/scripts/memory_manager.py --sync-vector

# 5. Generate report
python3 {baseDir}/scripts/memory_manager.py --summary

After Important Session

bash

# Extract key facts immediately
python3 {baseDir}/scripts/memory_manager.py --extract --today

# Update MEMORY.md
python3 {baseDir}/scripts/memory_manager.py --update --section "Key Decisions" --add "..."

Research Task

bash

# Search all memories for context
python3 {baseDir}/scripts/memory_manager.py --search "项目名" --include-archive --context 5

# Export relevant memories
python3 {baseDir}/scripts/memory_manager.py --export "项目名" --output project-context.md

Notes

• MEMORY.md is for main sessions only (not group chats)
• Daily files should be raw logs, not polished documents
• Archiving preserves data, just moves it out of active view
• Consolidation is distillation, not summarization
• The goal: quick recall of important information